Triple

T6715594
Position Surface form Disambiguated ID Type / Status
Subject Roland Garros Airport E153258 entity
Predicate hasFlightConnection P23780 FINISHED
Object Paris E568 NE FINISHED

How this triple was built (3 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Paris | Statement: [Roland Garros Airport, hasFlightConnection, Paris]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Paris
Context triple: [Roland Garros Airport, hasFlightConnection, Paris]
  • A. Paris
    Paris is a prince of Troy in Greek mythology, best known for judging the beauty contest of the goddesses and for abducting Helen, which sparked the Trojan War.
  • B. Paris chosen
    Paris is the capital and largest city of France, renowned for its historic architecture, art, fashion, and cultural influence worldwide.
  • C. Paris
    Paris is a major Chilean department store and retail chain offering a wide range of apparel, home goods, and consumer products.
  • D. Parigi
    Parigi is a coastal town that serves as the administrative center of Parigi Moutong Regency in Central Sulawesi, Indonesia.
  • E. Parisi
    Parisi is an Italian surname most notably associated with Giorgio Parisi, a Nobel Prize–winning theoretical physicist known for his work on complex systems and statistical mechanics.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: hasFlightConnection
Context triple: [Roland Garros Airport, hasFlightConnection, Paris]
  • A. operatesFlightsTo
    Indicates that one entity (typically an airline) runs or provides flight services to the location represented by the other entity.
  • B. hasScheduledFlights
    Indicates that there are one or more flights planned and set to occur between the related entities according to a schedule.
  • C. hasAirlines
    Indicates that one entity (such as an airport, city, or country) is served by or associated with one or more airline operators.
  • D. hasCityPair
    Indicates a relationship that links two cities considered as a connected or associated pair, often for purposes such as travel, trade, or comparison.
  • E. connectsWithAirport chosen
    Indicates that there is a direct transportation or operational link established between an entity and an airport.
  • F. None of above.

Provenance (4 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69c68809b4608190a2509ddb5ab87f05 completed March 27, 2026, 1:37 p.m.
NER Named-entity recognition batch_69c6d1246b748190aed94e8ab8625f7e completed March 27, 2026, 6:49 p.m.
NED1 Entity disambiguation (via context triple) batch_69c7006679688190af29dee17dcbf65d completed March 27, 2026, 10:10 p.m.
PD Predicate disambiguation batch_69c6d08c5d348190a29dee668c398e70 completed March 27, 2026, 6:46 p.m.
Created at: March 27, 2026, 2:07 p.m.